Dynamic Object Matching System for Substitute Procurement
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Solution Overview
Problem
Identifying substitutable substitutes for unavailable physical objects is challenging due to the vast number of possible objects and varied behaviors and characteristics associated with their requesters.
Innovation Solution
A system comprising hand-held electronic devices and a central computing system that dynamically learns object matching behavior based on past substitutions, similarities, and user preferences, using RF transceivers to identify and procure substitute objects from other geographic locations if needed, and prompting users for selections when suitable matches are not found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a system attempts to identify substitutable objects from a vast quantity of possible objects, then the ability to provide substitutes improves, but the complexity of the matching process increases
Solution Approach 1:
The patent segments the object matching process into multiple independent components: feature extraction modules that analyze individual object characteristics, similarity calculation modules that compare specific features, and ranking modules that prioritize substitutes. This segmentation allows the system to handle vast quantities of objects without overwhelming complexity by breaking down the matching task into manageable, modular operations.
Solution Approach 2:
The system changes parameters by transforming physical object characteristics into standardized digital features that can be efficiently processed and compared. By converting diverse object properties into uniform parameter sets, the system enables scalable matching across large object quantities while maintaining manageable computational complexity through consistent parameter transformation.
2Measurement precision
If the system considers varied behaviors and characteristics of requesters to improve substitution accuracy, then the quality of matches improves, but the data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and characteristics from requester data and object descriptions, rather than processing all available information. By selectively extracting key attributes that most influence substitution accuracy, the system maintains high match quality while significantly reducing the volume of data that requires processing and storage.
Solution Approach 2:
The system applies partial action by focusing computational resources on analyzing the most critical features and requester characteristics that have the greatest impact on substitution accuracy. Rather than exhaustively processing all possible data points, the system identifies and processes only the essential subset needed for accurate matching, thereby reducing overall data processing requirements while maintaining precision.
Data Source
AI summary
Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data. In response to matching data associated with one of the available physical objects to the data associated with the first physical object the central computing system may learn to replace the first physical object on the list with the one of the available physical objects based on dynamically learned matching behavior.